Deep learning (DL) technologies are becoming a buzzword these days, especially for breast histopathology image tasks, such as diagnosing, due to the high performance obtained in image classification. Among deep learning types, Convolutional Neural Networks (CNN) are the most common types of DL models utilized for medical image diagnosis and analysis. However, CNN suffers from high computation cost to be implemented and may require to adapt huge number of parameters. Thus, and in order to address this issue; several pre-trained models have been established with the predefined network architecture. In this study, a transfer learning model based on Visual Geometry Group with 16-layer deep model architecture (VGG16) is utilized to extract high-level features from the Break His benchmark histopathological images dataset. Then, multiple machine learning models (classifiers) are used to handle different Breast Cancer (BC) histopathological image classification tasks mainly: binary and multiclass with eight-class classifications. The experimental results on the public BreakHis benchmark dataset demonstrate that the proposed models are better than the previous works on the same dataset. Besides, the results show that the proposed models are able to outperform recent classical machine learning algorithms.